Arthur Mensch (Mistral AI) and John Collison (Stripe) fireside chat | Stripe AI DayโParis
TLDRArthur Mensch, co-founder of Mistral, discusses the company's unique approach to AI, emphasizing open-source models and their potential for customization and safety. He shares insights on the European AI landscape, the importance of understanding and controlling AI models, and the challenges of AI in various industries. Mensch also addresses the future of AI, including the potential for multimodal models and the ethical considerations surrounding AI development.
Takeaways
- ๐ช๏ธ Mistral, co-founded by Arthur Mensch, aims to be an open core LLM (Large Language Model) provider, differentiating itself from big US actors like Google and OpenAI.
- ๐ Arthur Mensch led the chinchilla paper at DeepMind, which is considered a foundational paper in AI.
- ๐ Mistral's name is inspired by the French word for wind, reflecting the company's European roots and commitment to AI.
- ๐ Mistral promotes an open approach to AI models, focusing on releasing model weights and creating a family of open source models.
- ๐ The company is working on releasing models that the community can improve upon, enhancing controllability and fine-tuning for specific purposes.
- ๐ง Mistral is also developing proprietary models and hosting solutions, but maintains a commitment to open core principles.
- ๐ Arthur Mensch believes that open source models are beneficial for AI safety, as they allow for better moderation and understanding of model behavior.
- ๐ค The conversation touches on the challenges of AI hallucinations and the importance of training models for longer periods to reduce them.
- ๐ France's AI renaissance is attributed to strong education in mathematics and computer science, as well as a growing tech ecosystem.
- ๐ Mistral is on track to release a small model soon, with bigger models in development, aiming to be cost-performance competitive.
- ๐ The discussion highlights the need for boldness in the European AI market, with calls for better capital and regulatory control to foster growth.
Q & A
What is the significance of the name 'Mistral' for the AI lab?
-Mistral is a French word for a type of wind, and it contains the vowels 'I' and 'A', which represent AI in French. The name symbolizes the lab's focus on AI technology.
What differentiates Mistral from other AI companies?
-Mistral differentiates itself by promoting an open approach to AI models, aiming to create and release open-source models with the goal of being the best in the open-source community.
How does Arthur Mensch view the role of open-source models in AI safety?
-Arthur believes that open-source models are beneficial for safety because they allow for transparency and the ability to tweak and moderate the models, which is not possible with closed-source models.
What is Mistral's stance on AI safety and controllability?
-Mistral emphasizes the importance of creating controllable models that can be adapted and moderated by downstream users, which requires access to the model's underlying weights.
How does Mistral plan to address the issue of AI hallucinations?
-Mistral plans to reduce hallucinations by training models for longer periods, using retrieval augmentation, and providing both generative and embedding models to improve accuracy and context adherence.
What is the current state of AI in France, and why is it considered a renaissance?
-France is experiencing an AI renaissance due to its strong educational system promoting mathematics and computer science, and the general renaissance of the French tech ecosystem, which is fostering innovation in generative AI.
What does Mistral aim to achieve within the next four years?
-Mistral aims to have the best technology within four years, starting with smaller models and progressively moving to larger ones, with a focus on cost-performance and addressing specific use cases.
How does Mistral plan to approach the development of multimodal models?
-While currently focusing on text, Mistral recognizes the gap in open-source multimodal models and plans to address it in the future, leveraging their experience in the field from their time at DeepMind.
What is Mistral's strategy for releasing their AI models?
-Mistral plans to release their models with a focus on being an open-core LLM provider, offering both open-source and proprietary models, with the goal of empowering the community to improve upon them.
How does Arthur Mensch view the potential for AI to develop consciousness?
-Arthur does not believe that current large language models possess consciousness, as they are primarily about storing and retrieving knowledge. He sees AI developing consciousness as a distant possibility.
Outlines
๐ Introduction and Mistral's Vision
The conversation begins with an introduction to Arthur Mensch, co-founder of Mistral, who transitioned from academia to industry and worked at DeepMind. The discussion focuses on Mistral's mission, which is to promote an open approach to AI models, releasing their weights and creating an open-source family of models. The importance of open-source models for AI safety and the potential for community improvement are highlighted. The conversation also touches on the upcoming release of Mistral's product and the company's stance on AI safety and open-source progress.
๐ Addressing AI Challenges and the French AI Renaissance
The discussion delves into the challenges of AI, such as hallucinations and the need for models to understand and process bad content. The importance of long training, knowledge cramming, and retrieval augmentation to reduce hallucinations is emphasized. The conversation also explores the French AI ecosystem, attributing its renaissance to the French education system and the general tech ecosystem. The need for boldness in founding new AI companies and controlling European regulation is discussed, along with the potential for interesting synergies in the open-source community.
๐ Regulation and AI Strategy
The conversation addresses the need for good AI regulation, such as well-documented and auditable models, and the pitfalls of hard threshold-based rules. The discussion also touches on the concept of artificial general intelligence (AGI) and Mistral's focus on creating adaptable models for enterprise use cases, rather than pursuing AGI. The strategy paper leak is acknowledged, and the company's goals for the best technology within four years are discussed, including the progression from small to larger models.
๐ Surprises, Speed, and AI's Future
Arthur shares his surprise at the speed of model development and the general rapid progress in the AI field. The conversation shifts to audience questions, addressing the sustainability of cloud computing for generative AI and the potential for on-device AI applications. The importance of understanding deep learning basics and gaining practical experience in AI development is emphasized, along with the need for patience and iterative learning in AI engineering.
๐ Open Source AI and Multimodal Models
The panel discusses the benefits of open-source AI, including cost efficiency and breaking proprietary barriers. The demand for better performance, lower costs, and reduced vendor lock-in is highlighted. The conversation also touches on Mistral's plans for multimodal models, acknowledging the gap in the open-source world and the company's intention to fill it. The importance of text as an encoding layer for various cognitive tasks is discussed, along with the limitations of current chess-playing AI models.
๐ ๏ธ Specialization, Training, and Collaboration
The discussion focuses on the specialization of large language models (LLMs) for specific use cases and the importance of incorporating domain-specific data into models. The conversation also explores the collaboration between academia and industry, particularly in the inference part of AI development. The potential for academia to contribute to model alignment, instruction, and specialization is acknowledged, and the importance of fostering such collaborations is emphasized.
๐ค Philosophical Questions and Future Predictions
The panel addresses philosophical questions about human consciousness and the possibility of creating artificial consciousness. The limitations of the Turing test and the need for better benchmarks to measure AI capabilities are discussed. The conversation concludes with a reflection on the exciting trends in AI and the anticipation of future surprises in the field.
Mindmap
Keywords
๐กMistral
๐กOpen Source Models
๐กAI Safety
๐กGenerative AI
๐กRetrieval Augmentation
๐กFrench AI Renaissance
๐กRegulation
๐กArtificial General Intelligence (AGI)
๐กOn-Device AI
๐กMultimodal Models
Highlights
Arthur Mensch, co-founder of Mistral, emphasizes the importance of open source models and the European approach to AI.
Mistral aims to be an open core LLM provider, differentiating from big US actors like Google and OpenAI.
Mistral's strategy includes releasing models and their weights to promote an open approach to AI development.
Arthur discusses the benefits of open source models for AI safety, including the ability to moderate and control content.
Mistral is working on releasing models that the community can improve upon, focusing on controllability and fine-tuning for specific purposes.
Arthur views Meta's release of Llama 2 as a strong opportunity for open source community synergy.
Mistral is set to release a small model soon, which is expected to be very good despite its size.
The importance of open source models in AI safety is highlighted, as understanding the model's workings is crucial for implementing policies.
Arthur shares his views on reducing hallucinations in AI models, including the need for extensive training and retrieval augmentation.
France's AI renaissance is attributed to its strong educational system and the general revival of the French tech ecosystem.
Arthur encourages boldness in founding new AI companies and suggests that Europe should not over-regulate AI to maintain competitiveness.
Mistral's goal is to produce models that empower people to work better and faster, rather than pursuing artificial general intelligence.
Arthur discusses the potential for on-device deployment of AI models, especially as models become smaller and more efficient.
Mistral is on track to release the best open source model by Q2 next year, starting with smaller models and progressing to larger ones.
Arthur shares his surprise at the speed of AI development and the capabilities of smaller models.
Mistral's strategy includes building a strong software arm to offer customization on specific data flows.
Arthur's experience in academia and industry collaboration, and his vision for future interactions between the two sectors.
Mistral's approach to AI safety involves understanding and controlling models, rather than censoring them behind closed APIs.
Arthur's perspective on the future of AI and the need for new benchmarks to measure and compare AI models.
Mistral's commitment to releasing well-documented models to foster community engagement and innovation.
